Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis

By M. Dalla Mura, Alberto Villa, Jon Atli Benediktsson, Jocelyn Chanussot, Lorenzo Bruzzone. IEEE Geoscience and Remote Sensing Letters, 2011

438 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/cb2bef072327dd1a9f7bc3bc15759c753ff99f0a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Shutao Li
2. Xudong Kang
3. Liangpei Zhang
4. Xin Huang
5. Jun Yu Li
6. Jon Atli Benediktsson
7. Zebin Wu
8. Guangrui Zhao

In Atmospheric Science

1. Shutao Li
2. Xudong Kang
3. Jun Yu Li
4. Liangpei Zhang
5. Jon Atli Benediktsson
6. Xin Huang
7. Lin He
8. Chenying Liu

In Media Technology

1. Shutao Li
2. Xudong Kang
3. Liangpei Zhang
4. Jon Atli Benediktsson
5. Jun Yu Li
6. Xin Huang
7. Lin He
8. Chenying Liu

Citations in the corpus

• PCA-Based Edge-Preserving Features for Hyperspectral Image Classification (2017, background): “This limitation is addressed by the use of the extended morphological attribute profiles (EMAPs) [25], [26]”
• An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery (2013, background): “However, it should be recognized that although various spatial features are currently available for high-resolution image processing, such as morphological features [6]–[10], [12], structural feature set [29], PSI [13], wavelet-based texture [30], object-based features [32], and GLCM [4], [33], it i”
• Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network (2017, neutral): “In other words, the spatial dependence is extracted in advance through various spatial filters, such as morphological profiles [10–12], entropy [13], attribute profiles [14], and low-rank representation [15,16].”
• Recent Advances on Spectral–Spatial Hyperspectral Image Classification: An Overview and New Guidelines (2018, methodology): “by more sophisticated morphological operations [42]–[45] are”
• Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification (2022, methodology): “The first few methods of SVM, EMAP, and 1-D-CNN clearly contain significant noise for the three datasets.”
• A Review of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information (2014, methodology): “Recently, a number of geographic information analysis approaches have been incorporated in texture extraction for improving remote sensing image classification accuracy [Van der Meer, 2012].”
• Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach (2016, neutral): “(EMPs), and attribute profiles [21].”
• A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, methodology): “Then, a large number of feature extraction methods that integrate the spatial structural and texture information with the spectral features have been developed, including morphological [21, 22, 23], filtering [24, 25], coding [26], etc.”